Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Kickback Cuts Backprop’s Red-Tape: Biologically Plausible Credit Assignment in Neural Networks
Authors: David Balduzzi, Hastagiri Vanchinathan, Joachim Buhmann
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present experiments demonstrating that Kickback matches Backprop s performance on standard benchmark datasets. and Experiments were performed on a 5-layer network with 2 output nodes, 10, 100 and 200 nodes in three hidden layers, and with the input layer directly drawn from the data. and We report normalized mean-squared errors. |
| Researcher Affiliation | Academia | David Balduzzi EMAIL Victoria University of Wellington Hastagiri Vanchinathan EMAIL ETH Zurich Joachim Buhmann EMAIL ETH Zurich |
| Pseudocode | Yes | Algorithm 1 (Kickback). The truncated feedback ϵj at node j is ϵj := β τj = global error influencej . |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or state that the code is publicly available. |
| Open Datasets | Yes | We present results on two robotics datasets, SARCOS1 and Barrett WAM2. ... 1Taken from www.gaussianprocess.org/gpml/data/. 2Taken from http://www.ias.tu-darmstadt.de/\Miscellaneous/ Miscellaneous. |
| Dataset Splits | Yes | Each SARCOS dataset consists of 44,484 training and 4,449 test points; Barrett split as 12,000 and 3,000. Parameters were tuned via grid-search with 5-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. It only mentions that experiments were implemented in Theano. |
| Software Dependencies | No | The paper states 'Experiments were implemented in Theano (Bergstra et al. 2010).' but does not provide a specific version number for Theano or any other software dependencies. |
| Experiment Setup | Yes | Experiments were performed on a 5-layer network with 2 output nodes, 10, 100 and 200 nodes in three hidden layers, and with the input layer directly drawn from the data. ... Training was performed in batch sizes of 20. ... No pretraining was used. We consider two network initializations. The first is uniform: draw weights uniformly at random from an interval symmetric about 0... The second initialization, signed is taken from Example 1: draw weights uniformly, then change their signs... |